Identificación partidaria
David Sulmont - Pontificia Universidad Católica del Perú
17 de mayo de 2015
INDICADORES SOBRE PARTIDOS POLÍTICOS EN LAPOP
Carga de los datos
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## else paste0(labels, : duplicated levels in factors are deprecated## [1] "year" "pais" "idnum" "weight1500"
## [5] "estratopri" "upm" "prov" "municipio_06"
## [9] "municipio08" "municipio10" "cluster" "ur"
## [13] "tamano" "idiomaq" "q1" "ls3"
## [17] "a4" "a1" "a2" "a3"
## [21] "a4i" "soct1" "soct2" "soct3"
## [25] "resp6" "idio1" "idio2" "idio3"
## [29] "idio4" "cp1" "cp2" "cp4a"
## [33] "cp4" "np1" "np1a" "np1b"
## [37] "np1c" "np2" "muni10" "sgl1"
## [41] "sgl2" "lgl2" "lgl2a" "lgl2b"
## [45] "lgl3" "muni5" "muni6" "muni8"
## [49] "muni9" "muni11" "muni15" "cp5_0406"
## [53] "cp5_0812" "cp6" "cp7" "cp8"
## [57] "cp9" "cp10" "cp11" "cp12"
## [61] "cp13" "cp20" "cp5a" "cp5b"
## [65] "cp5c" "cp5d" "cp5e" "ls6"
## [69] "ls6a" "it1" "it1a" "it1b"
## [73] "l1" "l1b" "it2" "it3"
## [77] "immig1" "immig2" "prot1" "prot2"
## [81] "prot3" "prot4" "y4" "jc1"
## [85] "jc4" "jc10" "jc12" "jc13"
## [89] "jc15" "jc16" "jc13a" "jc15a"
## [93] "jc16a" "gbmil1" "vic1ext" "vic1exta"
## [97] "aoj1" "aoj1a" "aoj1b" "vic2_0406"
## [101] "vic2_1012" "vic2aa" "vic1hogar" "vic20"
## [105] "vic21" "vic27" "aoj8" "aoj11"
## [109] "aoj11a" "aoj12" "aoj12a" "aoj16a"
## [113] "aoj17" "aoj18" "aoj9" "aoj16"
## [117] "aoj16b" "aoj19" "st1" "st2"
## [121] "st3" "st4" "b1" "b2"
## [125] "b3" "b4" "b6" "b10a"
## [129] "b11" "b13" "b14" "b12"
## [133] "b15" "b18" "b20" "b20a"
## [137] "b21" "b21a" "b31" "b32"
## [141] "b43" "b16" "b17" "b19"
## [145] "b33" "b37" "b23" "b42"
## [149] "b50" "b46" "b47" "b48"
## [153] "b40" "b45" "b39" "b51"
## [157] "b44" "resp0" "resp1" "resp2"
## [161] "resp3" "resp4" "resp5" "n1"
## [165] "n3" "n9" "n11" "n12"
## [169] "n15" "n10" "epp1" "epp2"
## [173] "epp3" "ec1" "ec2" "ec3"
## [177] "ec4" "wt1" "wt2" "m1"
## [181] "m2" "m10" "m11" "pop101"
## [185] "pop102" "pop103" "pop107" "pop113"
## [189] "pop106" "pop109" "pop110" "pop112"
## [193] "eff1" "eff2" "ing2" "ing4"
## [197] "pn2" "pn2a" "dem23" "ros1"
## [201] "ros2" "ros3" "ros4" "ros5"
## [205] "ros6" "rac3a" "rac3b" "rac3c"
## [209] "pn4" "pn5" "pn6" "e5"
## [213] "e8" "e11" "e15" "e14"
## [217] "e3" "e16" "e2" "d32"
## [221] "d33" "d34" "d36" "d37"
## [225] "d1" "d2" "d3" "d4"
## [229] "d5" "d6" "acr1" "abs5"
## [233] "dem6" "dem2" "dem11" "aut1"
## [237] "aut2" "aut2_04" "pp1" "pp2"
## [241] "dc1" "dc10" "dc13" "exc1"
## [245] "exc2" "exc4" "exc5" "exc6"
## [249] "exc11" "exc13" "exc14" "exc15"
## [253] "exc16" "exc17" "exc18" "exc19"
## [257] "exc7" "per1" "per2" "per3"
## [261] "per4" "per5" "per6" "per7"
## [265] "per8" "per9" "per10" "crisis1"
## [269] "crisis2" "vb1" "vb2" "vb3_10"
## [273] "vb3_08" "vb3_06" "vb7" "vb4"
## [277] "vb5" "vb8" "vb6" "vb50"
## [281] "vb60" "vb10" "vb11_10" "vb11_08"
## [285] "vb11_06" "vb12" "pol1" "pol2"
## [289] "dis2" "dis3" "dis4" "dis5"
## [293] "vb20" "vb61" "vb21" "sd1"
## [297] "sd2" "sd3" "sd4" "sd5"
## [301] "sd6" "sd7" "sd8" "sd9"
## [305] "sd10" "sd11" "sd12" "ls4"
## [309] "clien1" "clien2" "rac1c" "econ1a"
## [313] "econ1b" "econ1c" "econ1d" "econ2"
## [317] "rac4" "dis11" "dis17" "dis13"
## [321] "dis12" "rac1a" "rac1b" "rac1d"
## [325] "rac1e" "cct1" "dem13a" "dem13b"
## [329] "dem13c" "dem13d" "dem13" "pop1"
## [333] "pop2" "pop3" "pop4" "pop5"
## [337] "pop6" "pop7" "pop8" "pop9"
## [341] "pop10" "pop11" "pc1" "pc2"
## [345] "pc3" "pc5" "pc9" "pc12"
## [349] "pc14" "pc15" "pc19" "pc4"
## [353] "pc8" "pc13" "pc21" "der1"
## [357] "der2" "der3" "der4" "aa1"
## [361] "aa2" "aa3" "aa4" "exploit1"
## [365] "exploit2" "exploit5a" "exploit6" "exploit5b"
## [369] "paz1" "paz2" "paz3" "paz4"
## [373] "paz5" "lib1" "lib2" "lib3"
## [377] "lib4" "eref1" "eref2" "eref3"
## [381] "wc1" "wc2" "wc3" "ed"
## [385] "q2" "y1" "y2" "y3"
## [389] "haicr1" "q3c" "q3ca" "q3_08"
## [393] "q3_0406" "q5a" "q5b" "q4"
## [397] "q10" "q10a" "q10a_06" "q10a1"
## [401] "q10b" "q10a3" "q10c" "q16"
## [405] "q14" "q10d" "q10e" "q10f"
## [409] "q11" "q12" "q12a" "q13"
## [413] "q15" "etid" "leng1" "www1"
## [417] "ind1" "ind2" "ind3" "ind4"
## [421] "gi0" "gi1" "gi2" "gi3"
## [425] "gi5" "gi4" "gi6" "r1"
## [429] "r3" "r4" "r4a" "r5"
## [433] "r6" "r7" "r8" "r12"
## [437] "r14" "r15" "r16" "r18"
## [441] "r20" "r21" "r22" "r23"
## [445] "r24" "r25" "ocup4a" "ocup1a"
## [449] "ocup1a_04" "ocup1" "ocup1_04" "ocup1_06"
## [453] "ocup1b1" "ocup1b1_06" "ocup1b2" "ocup1anc"
## [457] "ocup12a" "ocup12" "ocup1c" "ocup27"
## [461] "ocup28" "ocup29" "ocup30" "ocup31"
## [465] "ocup31a" "ocup4" "desoc2" "desoc1"
## [469] "mig1" "mig2" "mig3" "pen1"
## [473] "pen3" "pen4" "sal1" "sal2"
## [477] "sal4" "colorr" "sexi" "colori"
## [481] "intid" "fecha" "ti" "ti3"
## [485] "order" "filter__" "municipio12" "vb3_12"
## [489] "vb11_12" "q10new" "canetid" "r26"
## [493] "odd" "gi7_12"Indicadores a trabajar
Personas que simpatizan con algún partido político
lapop$simp.part <- lapop$vb10 == "Sí"
tab.simp.part <- summarySE(data=lapop, measurevar="simp.part", groupvars=c("pais", "year"), na.rm=T, .drop=T)
tab.simp.part <- tab.simp.part[-1,]
tab.simp.part## pais year N simp.part sd se ci
## 2 México 2006 1560 0.4852564 0.4999428 0.012657795 0.02482810
## 3 México 2008 1560 0.3166667 0.4653254 0.011781333 0.02310893
## 4 México 2010 1562 0.2804097 0.4493433 0.011369405 0.02230092
## 5 México 2012 1560 0.3544872 0.4785110 0.012115174 0.02376375
## 6 Perú 2006 1500 0.2973333 0.4572369 0.011805806 0.02315765
## 7 Perú 2008 1500 0.1906667 0.3929578 0.010146127 0.01990211
## 8 Perú 2010 1500 0.2080000 0.4060122 0.010483190 0.02056328
## 9 Perú 2012 1500 0.1626667 0.3691844 0.009532299 0.01869806
## 10 Chile 2006 1517 0.2531312 0.4349488 0.011167228 0.02190485
## 11 Chile 2008 1527 0.2056320 0.4042951 0.010346154 0.02029418
## 12 Brasil 2006 1214 0.3278418 0.4696203 0.013478375 0.02644351
## 13 Brasil 2008 1497 0.2478290 0.4318963 0.011162682 0.02189617
## 14 Brasil 2010 2482 0.3049960 0.4604985 0.009243306 0.01812539
## 15 Brasil 2012 1500 0.2986667 0.4578260 0.011821016 0.02318749simp1 <- ggplot(tab.simp.part, aes(x=year, y=simp.part*100, shape=pais)) + geom_point(size=3.5) + xlab("Año") + ylab("% de entrevistados") + ylim(0, 60) + labs(shape="País") + theme_bw()
simp2 <- simp1 + ggtitle("Barómetro de las Américas: % de entrevistados que\n simpatizan con algún partido político,\n según año de la encuesta, por país") + theme(plot.title = element_text(lineheight=.8,face="bold"))
simp2
png(filename="/Users/David1/Dropbox/Doctorado/Tesis/Redaccion 2015/Tablas y graficos/Cap8_VotoIdeo/Simpartia partidos.png",
width = 7, height = 5, units = "in", res=200, antialias="none")
simp2
dev.off()## pdf
## 2Confianza en instituciones
Recodificación de variables de confianza en las instituciones
b21r <- as.numeric(lapop$b21)
b20r <- as.numeric(lapop$b20)
b12r <- as.numeric(lapop$b12)
b31r <- as.numeric(lapop$b31)
b10ar <- as.numeric(lapop$b10a)
b13r <- as.numeric(lapop$b13)
b14r <- as.numeric(lapop$b14)
b23r <- as.numeric(lapop$b23)
b37r <- as.numeric(lapop$b37)
b21r[b21r > 7] <- NA
b20r[b20r > 7] <- NA
b12r[b12r > 7] <- NA
b31r[b31r > 7] <- NA
b10ar[b10ar > 7] <- NA
b13r[b13r > 7] <- NA
b14r[b14r > 7] <- NA
b23r[b23r > 7] <- NA
b37r[b37r > 7] <- NA
b21r<-((b21r -1)/6)*100
b20r<-((b20r -1)/6)*100
b12r<-((b12r -1)/6)*100
b31r<-((b31r -1)/6)*100
b10ar<-((b10ar -1)/6)*100
b13r<-((b13r -1)/6)*100
b14r<-((b14r -1)/6)*100
b23r<-((b23r -1)/6)*100
b37r<-((b37r -1)/6)*100
lapop$c.part <- b21r
lapop$c.igle <- b20r
lapop$c.ffaa <- b12r
lapop$c.corte <- b31r
lapop$c.justi <- b10ar
lapop$c.cong <- b13r
lapop$c.gob <- b14r
lapop$c.sindi <- b23r
lapop$c.media <- b37rGráfico para el índice de confianza en las instituciones
library(doBy)## Loading required package: MASSconfianza <- summaryBy(c.part + c.igle + c.ffaa + c.gob + c.media + c.cong + c.justi ~ pais+year, data = lapop, FUN = function(x) {c(m=mean(x, na.rm=T))})
library(reshape)##
## Attaching package: 'reshape'
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## The following objects are masked from 'package:plyr':
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## rename, round_anyind.conf <- melt(confianza, id=c("year", "pais"))
library(car)
ind.conf$variable <- recode(ind.conf$variable, "'c.part.m'='Partidos'; 'c.igle.m'='Iglesia';
'c.ffaa.m'='FFAA'; 'c.gob.m'='Gobierno'; 'c.media.m'='Medios';
'c.cong.m'='Parlamento'; 'c.justi.m'='Justicia'")
conf.inst <- ggplot(ind.conf, aes(x=value, y=variable)) + geom_point() + facet_grid(pais~year)
conf.inst2 <- conf.inst + xlab("Indice de confianza en las instituciones") + ylab("Instituciones") + ggtitle("Barómetro de las Américas: Indice de confianza en instituciones\n según año de la encuesta y país") + theme_bw() + theme(plot.title = element_text(lineheight=.8,face="bold"))
conf.inst2## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_point).
png(filename="/Users/David1/Dropbox/Doctorado/Tesis/Redaccion 2015/Tablas y graficos/Cap8_VotoIdeo/Confianza Instituciones.png",
width = 7, height = 5, units = "in", res=200, antialias="none")
conf.inst2## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_point).dev.off()## pdf
## 2Gráfico para el indice de confianza en partidos
conf.part <- summarySE(data=lapop, measurevar="c.part", groupvars=c("year", "pais"), na.rm=T, .drop=T)
plot.confpart <- ggplot(conf.part, aes(x=year, y=c.part, shape=pais)) + geom_point(size=3.5) + ylim(0,50) + ylab("Indice de confianza en Partidos Políticos") + xlab("Año") + labs(shape="País") + theme_bw() + ggtitle("Barómetro de las Américas: Indice de confianza en\n partidos políticos, según año de la encuesta, por país") + theme(plot.title = element_text(lineheight=.8,face="bold"))
plot.confpart
png(filename="/Users/David1/Dropbox/Doctorado/Tesis/Redaccion 2015/Tablas y graficos/Cap8_VotoIdeo/Confianza partidos.png",
width = 7, height = 5, units = "in", res=200, antialias="none")
plot.confpart
dev.off()## pdf
## 2Representatividad de partidos
epp1r <- as.numeric(lapop$epp1)
epp2r <- as.numeric(lapop$epp2)
epp3r <- as.numeric(lapop$epp3)
epp1r[epp1r > 7] <- NA
epp2r[epp2r > 7] <- NA
epp3r[epp3r > 7] <- NA
lapop$epp1r <- ((epp1r-1)/6)*100
lapop$epp2r <- ((epp2r-1)/6)*100
lapop$epp3r <- ((epp3r-1)/6)*100
rep.part <- summaryBy(epp1r + epp3r ~ pais+year, data = lapop, FUN = function(x) {c(m=mean(x, na.rm=T))})
rep.part <- melt(rep.part, id=c("year", "pais"))
s.rep.part <- subset(rep.part, year == 2008 | year == 2012)
mf_labeller <- function(var, value){
value <- as.character(value)
if (var=="variable") {
value[value=="epp1r.m"] <- "a) Representan"
value[value=="epp3r.m"] <- "b) Escuchan"
}
return(value)
}
plot.rep.part <- ggplot(s.rep.part, aes(x=year, y=value, shape=pais)) + geom_point(size = 3.5) + ylim(10, 50) + facet_grid(.~ variable, labeller=mf_labeller) + xlab("Año") + ylab("Nivel") + labs(shape = "País") + theme_bw() + ggtitle("Barómetro de las Américas: ¿Hasta que punto los\n partidos políticos:a) Representan bien a sus votantes?;\n b) Escuchan a la gente como usted?;\n según año de la encuesta, por país") + theme(plot.title = element_text(lineheight=.8,face="bold"))
plot.rep.part
png(filename="/Users/David1/Dropbox/Doctorado/Tesis/Redaccion 2015/Tablas y graficos/Cap8_VotoIdeo/Representatividad partidos.png",
width = 7, height = 5, units = "in", res=200, antialias="none")
plot.rep.part
dev.off()## pdf
## 2Necesidad de los partidos para la democracia
dem23r <- as.numeric(lapop$dem23)
dem23r[dem23r > 7] <- NA
lapop$dem23r <- ((dem23r-1)/6)*100
part.neces <- summarySE(lapop, measurevar="dem23r", groupvars=c("pais", "year"), na.rm=T, .drop=T)## Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs producedpart.neces <- part.neces[-1, ]
g.part.neces <- ggplot(part.neces, aes(x=year, y=dem23r)) + geom_point(size=3) + ylim(0, 100) + facet_grid(.~ pais) + theme_bw() + xlab("Año") + ylab("Nivel de acuerdo") + ggtitle("Barómetro de las Américas: Nivel de acuerdo con la frase\n 'La democracia puede existir sin partidos políticos',\n según año y país") + theme(plot.title = element_text(lineheight=.8,face="bold"))
g.part.neces
png(filename="/Users/David1/Dropbox/Doctorado/Tesis/Redaccion 2015/Tablas y graficos/Cap8_VotoIdeo/Necesidad Partidos.png",
width = 7, height = 5, units = "in", res=200, antialias="none")
g.part.neces
dev.off()## pdf
## 2IDENTIFICACIÓN PARTIDARIA EN CSES
Se cargan los datos
load("/Users/David1/Dropbox/Doctorado/Data Analisis/Data/Datos procesados/cses.RData")
names(cses)## [1] "cses_mod" "eleccion" "eleccion_2"
## [4] "pais" "year" "id_r_cses"
## [7] "id_r_pais" "genero" "edad"
## [10] "educ" "ingreso" "urb_rur"
## [13] "voto_elec" "vote_pres" "vote_parl"
## [16] "cl_party_a" "cl_party_b" "cl_party_c"
## [19] "cl_party_d" "cl_party_e" "cl_party_f"
## [22] "cl_party_g" "cl_party_h" "cl_party_i"
## [25] "cl_party_o" "cl_noparty" "num_parties"
## [28] "mcl_party_a" "mcl_party_b" "mcl_party_c"
## [31] "mcl_party_d" "mcl_party_e" "mcl_party_f"
## [34] "mcl_party_g" "mcl_party_h" "mcl_party_i"
## [37] "mcl_party_o" "pref_party" "like_a"
## [40] "like_b" "like_c" "like_d"
## [43] "like_e" "like_f" "like_g"
## [46] "like_h" "like_i" "izde_ent"
## [49] "izde_a" "izde_b" "izde_c"
## [52] "izde_d" "izde_e" "izde_f"
## [55] "izde_g" "izde_h" "izde_i"
## [58] "e_izde_a" "e_izde_b" "e_izde_c"
## [61] "e_izde_d" "e_izde_e" "e_izde_f"
## [64] "e_izde_g" "e_izde_h" "e_izde_i"
## [67] "performance" "satdem" "power_dif"
## [70] "vote_dif" "conpol1" "conpol2"
## [73] "conpol3" "conpol_ag" "lpres_pref"
## [76] "polariz" "e_izde_b2" "filter_."
## [79] "rec_izde_ent" "gedad" "edad2"
## [82] "n.educ" "q.ing" "nom.part.pres"
## [85] "nom.part.parl" "alianza.p" "mismo.parl.pres"
## [88] "vpres.pref.party" "vparl.pref.party"Se hace un crosstab de partido por elección, se convierte la tabla en data frame para poder trabajarlo en excel y poner las siglas a los partidos políticos
pref.p <- prop.table(table(cses$eleccion, cses$pref_party),1)*100
pref.p <- as.data.frame(pref.p)
pref.p## Var1 Var2 Freq
## 1 BRA_2002 NINGUNO 37.57987220
## 2 BRA_2006 NINGUNO 58.20000000
## 3 BRA_2010 NINGUNO 41.95000000
## 4 CHL_1999 NINGUNO 64.21232877
## 5 CHL_2005 NINGUNO 52.22602740
## 6 CHL_2009 NINGUNO 60.66666667
## 7 MEX_1997 NINGUNO 56.32070831
## 8 MEX_2000 NINGUNO 49.60362401
## 9 MEX_2003 NINGUNO 38.15987934
## 10 MEX_2006 NINGUNO 32.43243243
## 11 MEX_2009 NINGUNO 33.87500000
## 12 PER_2000 NINGUNO 70.68965517
## 13 PER_2001 NINGUNO 59.66010733
## 14 PER_2006 NINGUNO 43.98422091
## 15 PER_2011 NINGUNO 40.76433121
## 16 BRA_2002 PARTIDO A 33.74600639
## 17 BRA_2006 PARTIDO A 5.00000000
## 18 BRA_2010 PARTIDO A 32.80000000
## 19 CHL_1999 PARTIDO A 23.71575342
## 20 CHL_2005 PARTIDO A 3.76712329
## 21 CHL_2009 PARTIDO A 5.33333333
## 22 MEX_1997 PARTIDO A 18.05213970
## 23 MEX_2000 PARTIDO A 20.44167610
## 24 MEX_2003 PARTIDO A 21.92056310
## 25 MEX_2006 PARTIDO A 26.71275927
## 26 MEX_2009 PARTIDO A 33.70833333
## 27 PER_2000 PARTIDO A 10.70780399
## 28 PER_2001 PARTIDO A 15.29516995
## 29 PER_2006 PARTIDO A 15.63116371
## 30 PER_2011 PARTIDO A 20.95541401
## 31 BRA_2002 PARTIDO B 6.74920128
## 32 BRA_2006 PARTIDO B 25.90000000
## 33 BRA_2010 PARTIDO B 6.50000000
## 34 CHL_1999 PARTIDO B 8.64726027
## 35 CHL_2005 PARTIDO B 7.36301370
## 36 CHL_2009 PARTIDO B 11.83333333
## 37 MEX_1997 PARTIDO B 8.60796852
## 38 MEX_2000 PARTIDO B 19.87542469
## 39 MEX_2003 PARTIDO B 24.58521870
## 40 MEX_2006 PARTIDO B 23.38152106
## 41 MEX_2009 PARTIDO B 18.75000000
## 42 PER_2000 PARTIDO B 9.61887477
## 43 PER_2001 PARTIDO B 16.90518784
## 44 PER_2006 PARTIDO B 19.92110454
## 45 PER_2011 PARTIDO B 15.60509554
## 46 BRA_2002 PARTIDO C 4.15335463
## 47 BRA_2006 PARTIDO C 5.90000000
## 48 BRA_2010 PARTIDO C 9.60000000
## 49 CHL_1999 PARTIDO C 0.00000000
## 50 CHL_2005 PARTIDO C 9.07534247
## 51 CHL_2009 PARTIDO C 9.16666667
## 52 MEX_1997 PARTIDO C 15.88785047
## 53 MEX_2000 PARTIDO C 7.98414496
## 54 MEX_2003 PARTIDO C 10.45751634
## 55 MEX_2006 PARTIDO C 16.65619107
## 56 MEX_2009 PARTIDO C 8.16666667
## 57 PER_2000 PARTIDO C 0.36297641
## 58 PER_2001 PARTIDO C 3.39892665
## 59 PER_2006 PARTIDO C 7.93885602
## 60 PER_2011 PARTIDO C 8.98089172
## 61 BRA_2002 PARTIDO D 10.58306709
## 62 BRA_2006 PARTIDO D 1.30000000
## 63 BRA_2010 PARTIDO D 0.15000000
## 64 CHL_1999 PARTIDO D 0.00000000
## 65 CHL_2005 PARTIDO D 7.79109589
## 66 CHL_2009 PARTIDO D 2.66666667
## 67 MEX_1997 PARTIDO D 0.88539105
## 68 MEX_2000 PARTIDO D 0.05662514
## 69 MEX_2003 PARTIDO D 2.61437908
## 70 MEX_2006 PARTIDO D 0.00000000
## 71 MEX_2009 PARTIDO D 3.16666667
## 72 PER_2000 PARTIDO D 1.45190563
## 73 PER_2001 PARTIDO D 1.61001789
## 74 PER_2006 PARTIDO D 3.69822485
## 75 PER_2011 PARTIDO D 6.36942675
## 76 BRA_2002 PARTIDO E 0.83865815
## 77 BRA_2006 PARTIDO E 1.50000000
## 78 BRA_2010 PARTIDO E 0.85000000
## 79 CHL_1999 PARTIDO E 0.00000000
## 80 CHL_2005 PARTIDO E 12.84246575
## 81 CHL_2009 PARTIDO E 5.91666667
## 82 MEX_1997 PARTIDO E 0.09837678
## 83 MEX_2000 PARTIDO E 0.50962627
## 84 MEX_2003 PARTIDO E 1.00553042
## 85 MEX_2006 PARTIDO E 0.12570710
## 86 MEX_2009 PARTIDO E 1.16666667
## 87 PER_2000 PARTIDO E 3.99274047
## 88 PER_2001 PARTIDO E 0.08944544
## 89 PER_2006 PARTIDO E 3.74753452
## 90 PER_2011 PARTIDO E 3.94904459
## 91 BRA_2002 PARTIDO F 0.55910543
## 92 BRA_2006 PARTIDO F 0.60000000
## 93 BRA_2010 PARTIDO F 0.85000000
## 94 CHL_1999 PARTIDO F 0.00000000
## 95 CHL_2005 PARTIDO F 3.85273973
## 96 CHL_2009 PARTIDO F 0.00000000
## 97 MEX_1997 PARTIDO F 0.09837678
## 98 MEX_2000 PARTIDO F 0.00000000
## 99 MEX_2003 PARTIDO F 0.75414781
## 100 MEX_2006 PARTIDO F 0.12570710
## 101 MEX_2009 PARTIDO F 0.75000000
## 102 PER_2000 PARTIDO F 0.54446461
## 103 PER_2001 PARTIDO F 0.08944544
## 104 PER_2006 PARTIDO F 1.87376726
## 105 PER_2011 PARTIDO F 2.22929936
## 106 BRA_2002 PARTIDO G 1.19808307
## 107 BRA_2006 PARTIDO G 0.00000000
## 108 BRA_2010 PARTIDO G 0.20000000
## 109 CHL_1999 PARTIDO G 0.00000000
## 110 CHL_2005 PARTIDO G 0.00000000
## 111 CHL_2009 PARTIDO G 2.00000000
## 112 MEX_1997 PARTIDO G 0.00000000
## 113 MEX_2000 PARTIDO G 0.00000000
## 114 MEX_2003 PARTIDO G 0.20110608
## 115 MEX_2006 PARTIDO G 0.25141420
## 116 MEX_2009 PARTIDO G 0.29166667
## 117 PER_2000 PARTIDO G 0.00000000
## 118 PER_2001 PARTIDO G 0.00000000
## 119 PER_2006 PARTIDO G 0.19723866
## 120 PER_2011 PARTIDO G 0.00000000
## 121 BRA_2002 PARTIDO H 1.31789137
## 122 BRA_2006 PARTIDO H 0.00000000
## 123 BRA_2010 PARTIDO H 0.65000000
## 124 CHL_1999 PARTIDO H 0.00000000
## 125 CHL_2005 PARTIDO H 0.00000000
## 126 CHL_2009 PARTIDO H 0.08333333
## 127 MEX_1997 PARTIDO H 0.00000000
## 128 MEX_2000 PARTIDO H 0.00000000
## 129 MEX_2003 PARTIDO H 0.20110608
## 130 MEX_2006 PARTIDO H 0.31426776
## 131 MEX_2009 PARTIDO H 0.12500000
## 132 PER_2000 PARTIDO H 0.00000000
## 133 PER_2001 PARTIDO H 0.00000000
## 134 PER_2006 PARTIDO H 0.00000000
## 135 PER_2011 PARTIDO H 0.00000000
## 136 BRA_2002 PARTIDO I 0.19968051
## 137 BRA_2006 PARTIDO I 0.00000000
## 138 BRA_2010 PARTIDO I 0.25000000
## 139 CHL_1999 PARTIDO I 0.00000000
## 140 CHL_2005 PARTIDO I 0.00000000
## 141 CHL_2009 PARTIDO I 0.50000000
## 142 MEX_1997 PARTIDO I 0.00000000
## 143 MEX_2000 PARTIDO I 0.00000000
## 144 MEX_2003 PARTIDO I 0.05027652
## 145 MEX_2006 PARTIDO I 0.00000000
## 146 MEX_2009 PARTIDO I 0.00000000
## 147 PER_2000 PARTIDO I 0.00000000
## 148 PER_2001 PARTIDO I 0.00000000
## 149 PER_2006 PARTIDO I 0.00000000
## 150 PER_2011 PARTIDO I 0.00000000
## 151 BRA_2002 OTRO PARTIDO 3.07507987
## 152 BRA_2006 OTRO PARTIDO 1.60000000
## 153 BRA_2010 OTRO PARTIDO 6.20000000
## 154 CHL_1999 OTRO PARTIDO 3.42465753
## 155 CHL_2005 OTRO PARTIDO 3.08219178
## 156 CHL_2009 OTRO PARTIDO 1.83333333
## 157 MEX_1997 OTRO PARTIDO 0.04918839
## 158 MEX_2000 OTRO PARTIDO 1.52887882
## 159 MEX_2003 OTRO PARTIDO 0.05027652
## 160 MEX_2006 OTRO PARTIDO 0.00000000
## 161 MEX_2009 OTRO PARTIDO 0.00000000
## 162 PER_2000 OTRO PARTIDO 2.63157895
## 163 PER_2001 OTRO PARTIDO 2.95169946
## 164 PER_2006 OTRO PARTIDO 3.00788955
## 165 PER_2011 OTRO PARTIDO 1.14649682Se importan los datos trabajados en excel
id.part0 <- read.csv("/Users/David1/Dropbox/Doctorado/Data Analisis/Data/Datos procesados/part_idAL.csv", sep=";")
id.part <- subset(id.part0, partid!="NINGUNO")Se preparan los datos y se crea un gráfico con el % de personas que no se identifican con ningún partido, según elección, por país.
id.part2 <- subset(id.part0, partid=="NINGUNO")
id.part2$idp <- 100-id.part2$freqpref
png("/Users/David1/Dropbox/Doctorado/Tesis/Redaccion 2015/Tablas y Graficos/Cap8_VotoIdeo/csesidpart.png", width = 7, height = 5, units = "in", res = 200)
dotchart(id.part2$idp, labels=id.part2$year, cex= 0.7, pch=19, xlim=c(0,70),
groups=id.part2$pais,
main="CSES: Porcentaje de entrevistados que se identifican con algún partido político,\n según país y elección")
dev.off()## pdf
## 2Se crea una función para producir los gráficos de identificación por partido, por país y elección.
graf.idp <- function(p){
data <- subset(id.part, pais==p)
data <- data[order(data$freqpref),]
data$ano <- as.factor(data$year)
dotchart(data$freqpref,labels=data$nompart,cex=.7,pch=19, xlim=c(0, 40), groups= data$ano,
main= paste(p, ": Porcentaje de electores que se identifican con un\n partido político, según partido y elección"),
xlab="% de electores")}Se generan los gráficos para cada país
png("/Users/David1/Dropbox/Doctorado/Tesis/Redaccion 2015/Tablas y Graficos/Cap8_VotoIdeo/idpart_brasil.png", width = 7, height = 5, units = "in", res = 200)
graf.idp("BRASIL")
dev.off()## pdf
## 2png("/Users/David1/Dropbox/Doctorado/Tesis/Redaccion 2015/Tablas y Graficos/Cap8_VotoIdeo/idpart_chile.png", width = 7, height = 5, units = "in", res = 200)
graf.idp("CHILE")
dev.off()## pdf
## 2png("/Users/David1/Dropbox/Doctorado/Tesis/Redaccion 2015/Tablas y Graficos/Cap8_VotoIdeo/idpart_mexico.png", width = 7, height = 5, units = "in", res = 200)
graf.idp("MEXICO")
dev.off()## pdf
## 2png("/Users/David1/Dropbox/Doctorado/Tesis/Redaccion 2015/Tablas y Graficos/Cap8_VotoIdeo/idpart_peru.png", width = 7, height = 5, units = "in", res = 200)
graf.idp("PERU")
dev.off()## pdf
## 2PREFERENCIA POR PARTIDOS POLÍTICOS
Se genera una base de datos con la media de la preferencia (variable like) por cada partido político
library(doBy)
data <- summaryBy(like_a + like_b + like_c + like_d + like_e + like_f + like_g + like_h + like_i ~ pais + year, data = cses,
FUN = function(x){c(m = mean(x, na.rm=T))})
library(reshape)
data2 <- melt(data, id=c("pais", "year"))
data3 <- data2[data2$value != "NaN", ]
data4 <- data3[order(data3$pais, data3$year),]
partido <- c("CONCERT", "APCH","PC","UDI","PDC","PPD","RN","PS","PC","UDI","RN","PDC","PPD",
"PS","PC","PRI","PAN","PRD","PVEM","PT","PCARD","PAN","PRI","PRD","PT","PVEM","PARM","PAN",
"PRI","PRD","PVEM","PT","Convergencia","PAN","PRD","PRI","PVEM","PT","Convergencia","PANAL",
"PSD","PRI","PAN","PRD","PVEM","PT","PANAL","Convergencia","PSD","P2000","PP","FIM","SP",
"PAP","UN","PP","PAP","UN","FIM","SOLPOP","RENAC","UPP","PAP","UN","AF","FDC","RN","PP",
"GP","F2011","APGC","PP","SN","PAP","PT","PSDB","PFL","PMDB","PDT","PTB","PMDB","PT",
"PSDB","PFL","PDT","PTB","PT","PMDB","PSDB","DEM","PDT","PTB")
data4$partido <- partidoFunción para realizar los gráficos
graf.like <- function(p){
data <- subset(data4, pais==p)
data <- data[order(data$value),]
data$ano <- as.factor(data$year)
dotchart(data$value,labels=data$partido,cex=.7,pch=19, xlim=c(0, 10), groups= data$ano,
main=paste(p, ": Grado de preferencia por los partidos políticos\n según elección"),
xlab="0 = Nada; 10 = Mucho")}Se generan los gráficos por país
graf.like("BRASIL")
graf.like("PERU")
graf.like("CHILE")
graf.like("MEXICO")